This project is about confluence and the related unique normal form property of rewrite systems. In the preceding years many powerful confluence methods have been developed and implemented in confluence tools that participate in the recently established confluence competition. Important first steps towards certification have been made, but much remains to be done. The aim of this successor project is to fill two important gaps concerning certification, investigate methods for the unique normal form property, study various complexity issues related to confluence and unique normal forms, and further develop the confluence tool CSI and the certification tool CeTA for confluence.

The potential of existing programming models to effectively utilise future Exascale systems, while addressing the challenges of energy-efficiency, diminishing resilience and hardware diversity, is severey limited. It follows that the lack of appropriate, high-productivity and portable programming models for Exascale computing is a fundamental barrier for the future of science and engineering. We propose the AllScale environment for the effective development of highly scalable, resilient and performance-portable parallel applications for Exascale systems.

Clutter in an open world is a challenge for many aspects of robotic systems, especially for autonomous robots deployed in unstructured domestic settings, affecting navigation, manipulation, vision, human robot interaction and planning. SQUIRREL addresses these issues by actively controlling clutter and incrementally learning to extend the robot's capabilities while doing so. We term this the B3 (bit by bit) approach, as the robot tackles clutter one bit at a time and also extends its knowledge continuously as new bits of information become available. SQUIRREL is inspired by a user driven scenario, that exhibits all the rich complexity required to convincingly drive research, but allows tractable solutions with high potential for exploitation. We propose a toy cleaning scenario, where a robot learns to collect toys scattered in loose clumps or tangled heaps on the floor in a child's room, and to stow them in designated target locations.

The radioulnar joint is one of two joints of the forearm bones radius and ulna. The bone surfaces enable forearm motion (i.e., pro-supination) in an extensive range while the stability is primarily maintained by a complex system of ligaments connecting the bones. Soft tissue injury can cause chronic joint instability, pain, or functional disability, thus requiring operative treatment by anatomical reconstruction. The goal of this project is to develop a patient-specific hard- and soft-tissue model for simulating not only the healthy but also the pathological forearm motion with respect to functional disability and instability. Besides improving the diagnosis of soft tissue associated injuries before an intervention, such as for distal radius instability, the ultimate goal is to predict the surgical outcome by analyzing the forearm motion before and after simulated surgery.

Collaborative systems support the cooperation of stakeholders across organisations and system boundaries. Due to their evolving nature and their strong quality requirements (e.g. concerning security and dependability) the engineering of collaborative systems poses immense challenges. Living Models strives to a model-based methodology for the continuous quality management of collaborative systems.

Some progress has been made in understanding and managing cybercrime as well assessing its economic impact. Yet much remains to be done. Lack of co-ordination in law enforcement and legislation, lack of common consensus on the nature of cybercrime and lack of knowledge sharing and trust are just some of the issues that both afflict cybercrime responses and cloud our understanding of cyber crime. E-CRIME addresses these well-known problems, while analysing the economic impact of cybercrime and developing concrete measures to manage risks and deter cybercriminals in non-ICT sectors.

The Big data roadmap and cross-disciplinarY community for addressing socieTal Externalities (BYTE) project will assist European science and industry in capturing the positive externalities and diminishing the negative externalities associated with big data in order to gain a greater share of the big data market by 2020.

The H2020 project READ (Recognition and Enrichment of Archival Documents) which is coordinated by the Digitisation and Digital Preservation group of the University of Innsbruck is an e-Infrastructure project funded by the European Commission with 8,2 mill. EUR. The project combines research, services and network building. It is focused on making archival material more accessible through the development of cutting-edge technologies, namely Handwritten Text Recognition (HTR). Leading research groups from Germany, Spain, UK, Austria, Finland and Greece are taking part in the project and will set new standards in Handwritten Text Recognition, Key Word Spotting, Layout Analysis, Automatic Writer Identification and related fields.

The HPC facilities at the University of Innsbruck will be used to carry out experiments for training neural networks on historical handwriting, as well as to recognize large amounts (millions of pages) of historical documents. A first prototype can be accessed and tried out from the following website: http://transkribus.eu/